Comments (4)
Hi Doug,
this seems to be caused by the 'parallel' pkg (tuning steps are parallelized in sjSDM_cv). I will upgrade one of my systems to R4.0 and get to the bottom of this.
But for now you can turn off the parallelization in sjSDM_cv by setting n_cores=NULL (and it should hopefully work).
from s-jsdm.
and...it works! (although with n_cores = NULL
, R is using six cores)
from s-jsdm.
Yes and no, pytorch should always use all of your cpu cores, but sometimes (if you have really small models) it is efficient to parallelize additionally over the pytorch models from within R 😅
from s-jsdm.
ah.
from s-jsdm.
Related Issues (20)
- VENN diagram Anova HOT 1
- Problem with installation HOT 1
- Error in sjSDM_cv HOT 8
- PyTorch not installed HOT 13
- Is there a way to assess the significance (p-value?) of pairwise relationships in species associations? HOT 2
- ValueError: Expected parameter rate HOT 4
- Random factors HOT 19
- ValueError: Expected value argument HOT 3
- vp codist
- sjSDM on a remote server HOT 3
- sjSDM - memory issues of anova() HOT 6
- Installation error HOT 5
- Failing to run models on GPU
- Readme Files
- plotsjSDMcoef returns summary table / summary can't run invisible
- importance
- Exact VP definition HOT 1
- Internal structure set negative R2 to 0 or scale
- Bias in sjSDM::linear() coefficient estimates when including an intercept term. HOT 3
- Unable to scale negative R squareds HOT 1
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